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Course Outline
Introduction to Google AI Studio
- Overview of Google AI Studio and its capabilities.
- Setting up a workspace and exploring the interface.
- Understanding AI project workflows in Google AI Studio.
Data Preparation and Management
- Importing and preprocessing datasets.
- Exploring data visualization tools.
- Ensuring data quality for AI projects.
Model Training and Optimization
- Using AutoML for rapid model development.
- Custom model training with TensorFlow and PyTorch.
- Hyperparameter tuning and performance optimization.
Model Deployment and Scaling
- Deploying models as REST APIs.
- Integrating models with Google Cloud infrastructure.
- Scaling AI services for production use.
Leveraging Advanced Features
- Implementing Explainable AI (XAI) practices.
- Utilizing Google AI APIs for vision, language, and more.
- Exploring pre-trained models and transfer learning.
Monitoring and Troubleshooting
- Monitoring deployed models for performance.
- Analyzing model predictions and feedback.
- Troubleshooting common issues in AI workflows.
Real-World Applications
- Case studies of AI solutions powered by Google AI Studio.
- Building a complete AI project from start to finish.
Summary and Next Steps
Requirements
- A solid grasp of machine learning concepts and frameworks.
- Proficiency in Python programming.
- Familiarity with Google Cloud services is advisable.
Audience
- AI developers.
- Machine learning engineers.
- Data scientists.
21 Hours
